Search Results (5,308)

Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed with an interacting multiple model (IMM) approach. During multiple-target tracking, tracks are initialized, maintained, and terminated. There are three different associations in track maintenance: measurement–track association, track–track association for tracks that exist at the same time (track association and fusion), and track–track association for tracks that exist at separate times (TSA). Measurement–track association selects the statistically nearest measurement and updates the track with the measurement through the IMM filter. Track association and fusion fuses redundant tracks for the same target that are spatially separated. TSA connects tracks that have become broken and separated over time. This process is accomplished through the selection of candidate track pairs, backward IMM filtering, association testing, and an assignment rule. In the experiments, a drone was equipped with an infrared thermal imaging camera, and two thermal videos were captured of three people in a non-visible environment. These three hikers were located close together and occluded by each other or other obstacles in the mountains. The drone was allowed to move arbitrarily. The tracking results were evaluated by the average total track life, average mean track life, and average track purity. The track segment association improved the average mean track life of each video by 99.8% and 250%, respectively Full article

This study supports Jamaica’s renewable energy implementation strategies by providing updated wind atlases and identifying suitable locations for future wind farms. Using a GIS-based Analytic Hierarchy Process with multi-criteria decision-making (AHP-MCDM), this research integrates stakeholders’ opinions, environmental considerations, and technical factors to assess land suitability for wind energy development. The analysis reveals that Jamaica has the potential to increase its wind power output by 8.99% compared to the current production of 99 MW. This expansion could significantly contribute to offsetting fossil fuel-based energy consumption and reducing carbon dioxide emissions. It identifies sites across several parishes, including Westmoreland, Clarendon, St. Mary, and St. James, as highly suitable for utility-scale wind farm development. By providing detailed spatial information and estimated energy outputs, this research offers valuable insights for energy planners, investors, and policymakers to create sustainable energy policies and advance Jamaica’s 50% renewable energy goal by 2030. Full article

The Shibaogou pluton, located in the Luanchuan orefield of western Henan Province in China, is a typical porphyritic granite within the Yanshanian “Dabie-type” Mo metallogenic system. It is mainly composed of porphyritic monzogranite and porphyritic syenogranite. Zircon U-Pb dating results indicate emplacement ages of 150.1 ± 1.3 Ma and 151.0 ± 1.1 Ma for the monzogranite and 148.1 ± 1.0 Ma and 148.5 ± 1.3 Ma for the syenogranite. The pluton is characterized by geochemical features of high silicon, metaluminous, and high-K calc-alkaline compositions, enriched in Rb, U, Th, and Pb, and exhibits high Sr/Y (18.53–58.82), high (La/Yb)_N (9.01–35.51), and weak Eu anomalies. These features indicate a source region from a thickened lower crust with garnet and rutile as residual phases at depths of approximately 40–60 km. Sr-Nd-Pb isotopic analyses suggest that the magmatic source is mainly derived from the Taihua and Xiong’er Groups of the Huaxiong Block, mixed with juvenile crustal rocks from the Kuanping and Erlangping Groups of the North Qinling Accretion Belt. Combined with geological and isotopic characteristics, it is concluded that the Shibaogou pluton formed during the compression–extension transition period associated with the collision between the Yangtze Block and the North China Craton, reflecting the complex partial melting processes in the thickened lower crust. The present study reveals that the magmatic–hydrothermal activity at Shibaogou lasted approximately 5 Ma, showing multi-phase characteristics, further demonstrating the close relationship between the pluton and the Mo-W mineralization. Full article

Background: In agricultural supply chains, unequal bargaining power often leads to economic inequality, particularly for farmers. The fair profit distribution (FPD) approach offers a solution by optimizing supply chain flows (materials, information, and money) to promote economic equity among members. However, our literature review highlights a gap in applying the FPD approach to the facility location-allocation problem in supply chain network design (SCND), particularly in sugarcane-derived biofuel supply chains. Methods: Consequently, we propose a multi-period optimization model based on FPD to design a sugarcane biofuel supply chain. The methodology involves four steps: constructing a conceptual model, developing a mathematical model, designing a solution strategy, and generating insights. This model considers both investment (crop development, biorefinery construction) and operational phases over a long-term planning horizon, focusing on farm location and crop allocation. Results: By comparing the FPD model to a traditional centralized planning supply chain (CSC) approach, we examine the impact of the planning horizon, number of farms, and sugarcane prices paid by biorefineries on financial performance. While the FPD model results in lower overall system profits, it fosters a fairer economic scenario for farmers. Conclusions: This study contributes to economic justice in supply chains and offers insights to promote fair trade among stakeholders. Full article

To address the issues arising from the integration of a high proportion of distributed generation (DG) into the distribution network, which has led to the transition from traditional single-source to multi-source distribution systems, resulting in increased complexity of the distribution network topology and difficulties in fault localization, this paper proposes a fault localization method based on graph convolutional networks (GCNs) for distribution networks with a high proportion of distributed generation. By abstracting busbars and lines into graph structure nodes and edges, GCN captures spatial coupling relationships between nodes, using key electrical quantities such as node voltage magnitude, current magnitude, power, and phase angle as input features to construct a fault localization model. A multi-type fault dataset is generated using the Matpower toolbox, and model training is evaluated using K-fold cross-validation. The training process is optimized through early stopping mechanisms and learning rate scheduling. Simulations are conducted based on the IEEE 33-node distribution network benchmark, with photovoltaic generation, wind generation, and energy storage systems connected at specific nodes, validating the model’s fault localization capability under various fault types (single-phase ground fault, phase-to-phase short circuit, and line open circuit). Experimental results demonstrate that the proposed model can effectively locate fault nodes in complex distribution networks with high DG integration, achieving an accuracy of 98.5% and an AUC value of 0.9997. It still shows strong robustness in noisy environments and is significantly higher than convolutional neural networks and other methods in terms of model localization accuracy, training time, F1 score, AUC value, and single fault detection inference time, which has good potential for practical application. Full article

Remote sensing image retrieval (RSIR) plays a crucial role in remote sensing applications, focusing on retrieving a collection of items that closely match a specified query image. Due to the advantages of low storage cost and fast search speed, deep hashing has been one of the most active research problems in remote sensing image retrieval. However, remote sensing images contain many content-irrelevant backgrounds or noises, and they often lack the ability to capture essential fine-grained features. In addition, existing hash learning often relies on random sampling or semi-hard negative mining strategies to form training batches, which could be overwhelmed by some redundant pairs that slow down the model convergence and compromise the retrieval performance. To solve these problems effectively, a novel Deep Multi-similarity Hashing with Spatial-enhanced Learning, termed DMsH-SL, is proposed to learn compact yet discriminative binary descriptors for remote sensing image retrieval. Specifically, to suppress interfering information and accurately localize the target location, by introducing a spatial enhancement learning mechanism, the spatial group-enhanced hierarchical network is firstly designed to learn the spatial distribution of different semantic sub-features, capturing the noise-robust semantic embedding representation. Furthermore, to fully explore the similarity relationships of data points in the embedding space, the multi-similarity loss is proposed to construct informative and representative training batches, which is based on pairwise mining and weighting to compute the self-similarity and relative similarity of the image pairs, effectively mitigating the effects of redundant and unbalanced pairs. Experimental results on three benchmark datasets validate the superior performance of our approach. Full article

With the increasing demands of urban populations, achieving a balance between the supply and demand in the spatial allocation of urban green park spaces (UGSs) is essential for effective urban planning and improving residents’ quality of life. The study of UGS supply and demand balance has become a research hotspot. However, existing studies of UGS supply and demand balance rarely simultaneously improve the supply side, demand side, and transportation methods that connect the two, nor do they conduct a comprehensive, multi-dimensional supply and demand evaluation. Therefore, this study evaluates the accessibility of UGS within Hefei’s built-up areas, focusing on age-specific demands for UGS and incorporating various travel modes, including walking, cycling, driving, and public transportation. An improved two-step floating-catchment area (2SFCA) method is applied to evaluate the accessibility of UGS in Hefei’s built-up areas. This evaluation combines assessments using the Gini coefficient, Lorenz curve, location entropy, and local spatial autocorrelation analysis, utilizing the ArcGIS 10.8 and GeoDa 2.1 platforms. Together, these methods enable a supply–demand balance analysis of UGSs to identify areas needing improvement and propose corresponding strategies. The research results indicate the following: (1) from a regional perspective, there are significant disparities in the accessibility of UGS within Hefei’s urban center, with the old city showing more imbalance than the new city. Areas with high demand and low supply are primarily concentrated in the old city, which require future improvement; (2) in terms of travel modes, higher-speed travel (such as driving) offers better and more equitable accessibility compared to slower modes (such as walking), highlighting transportation as a critical factor influencing accessibility; (3) regarding population demand, there is an overall balance in the supply of UGS, with local imbalances observed in the needs of residents across different age groups. Due to the high specific demand for UGS among older people and children, the supply and demand levels in these two age groups are more consistent. This study offers valuable insights for achieving the balanced, efficient, and sustainable development of the social benefits of UGS. Full article

Screw pumps’ faulty working conditions affect the stability of oil production. At project sites, different sensors are used simultaneously to collect multi-dimensional signals; the data fault labels and location are not clear, and how to comprehensively use multi-source information in effective fault feature extraction has become an urgent issue. Existing diagnostic methods use a single signal or part of a signal and do not fully utilize the acquired signal, which makes it difficult to achieve the required accuracy of diagnostic results. This paper focuses on the model-driven approach to extract multi-source fault features of screw pumps. Firstly, it constructs a fault data model (FDM) by analyzing the fault mechanism of the screw pump. Secondly, it uses the FDM to select an effective data set. Thirdly, it constructs a multi-dimensional fault feature extraction model (MDFEM) to extract featured signal features and data features, for which we also comprehensively used multi-source signals in effective fault feature extraction, while other traditional methods only use one or two signals. Finally, after feature selection, unsupervised fault diagnosis was achieved by using the k-means method. After experimental verification, the method can comprehensively use multi-source information to construct an effective data set and extract multi-dimensional, effective fault features for screw pump fault diagnosis. Full article

The water temperature of lakes is one of their fundamental characteristics, upon which numerous processes in lake ecosystems depend. Therefore, it is crucial to have detailed knowledge about its changes and the factors driving those changes. In this article, a neural network model was developed to examine the impact of meteorological variables on lake water temperature by integrating daily meteorological data with data on interday variations. Neural networks were selected for their ability to model complex, non-linear relationships between variables, often found in environmental data. Among various architectures, the Artificial Neural Network (ANN) was chosen due to its superior performance, achieving an R² of 0.999, MSE of 0.0352, and MAE of 0.1511 in validation tests. These results significantly outperformed other models such as Multi-Layer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM). Two lakes (Lake Mikołajskie and Sławskie) differing in morphometric parameters and located in different physico-geographical regions of Poland were analyzed. Performance metrics for both lakes show that the model is capable of providing accurate water temperature forecasts, effectively capturing the primary patterns in the data, and generalizing well to new datasets. Key variables in both cases turned out to be air temperature, while the response to wind and cloud cover exhibited diverse characteristics, which is a result of the morphometric features and locations of the measurement sites. Full article

Background: Bouquet is a crucial characteristic indicative of wine quality that develops during the aging stage. The cytochrome P450 VvCYP76F14 multi-functionally catalyzes linalool into (E)-8-hydroxylinalool, (E)-8-oxolinalool, and (E)-8-carboxylinalool, which are direct precursors for wine bouquet. Wine bouquet was closely related to VvCYP76F14 activities. Method: The VvCYP76F14 genes were cloned from five wine grape varieties using a homologous cloning method. The variation in residues of VvCYP76F14s were assessed by multiple alignment of amino acid sequences. Functional studies were implemented by in vitro enzyme activity and transient over-expression systems. Results: D299T variation was observed in VvCYP76F14s of ‘Yantai 2-2-08’, ‘Yantai 2-2-19’, and ‘Yantai 2-3-37’ offspring lines, which was correlated with the decreased content of wine bouquet precursors of (E)-8-hydroxylinalool, (E)-8-oxolinalool, and (E)-8-carboxylinalool, respectively. Notably, the key amino acid residue D299 was located at the phase 0 intron positions of VvCYP76F14 genes isolated from distinct wine grape varieties or offspring lines, respectively. Notably, VvCYP76F14s of the ‘Yantai2-2-08’, ‘Yantai 2-2-19’, and ‘Yantai 2-3-37’ mutant lines exhibited lower in vitro enzymatic activities than those of ‘L35’ and ‘Merlot’. In addition, the transient expression of VvCYP76F14 cloned from ‘L35’ and ‘Merlot’ restored the levels of wine bouquet precursors in berries of three D299T mutant lines, respectively, whereas VvCYP76F14 cloned from D299T mutant lines failed. Conclusions: D299T variation was observed in three offspring lines and D299T mutation in VvCYP76F14 led to the decrease in wine bouquet precursor contents. VvCYP76F14 was implicated in the regulation of wine bouquet precursors in wine grapes. Full article

Drought has become an important factor affecting the environment and socio-economic sustainable development in northern China due to climate change. This study utilized the Standardized Precipitation Index (SPI) as a drought metric to investigate the correlation between drought characteristics and different grades of precipitation and rain days. The analysis was based on a long-term time series of precipitation data obtained from 116 meteorological stations located in Inner Mongolia, spanning 1960 to 2019. To achieve the objectives of the current research, the daily precipitation was categorized into four grades based on the “24-h Precipitation Classification Standard”, and the frequency of rain days for each grade was determined. Subsequently, the SPI was calculated for 1 and 12 months, enabling the identification of drought events. The results revealed pronounced spatiotemporal regional variations and complexities in the dry–wet climatic patterns of Inner Mongolia, with significant decreases in precipitation emerging as the primary driver of drought occurrences. Approximately 6% of the entire study period experienced short-term drought, while long-term drought periods ranged from 23% to 38%. Regarding multi-year trends, precipitation exhibited a weak increasing trend, while rain days exhibited a weak decreasing trend. Drought exhibited an alleviating trend, with 92% of stations displaying coefficients > 0 for SPI_Month and over 62% of stations displaying coefficients > 0 for SPI_Year. At the monthly scale, drought was most correlated with light rainfall trends and least correlated with moderate rainfall trends. At the annual scale, drought was relatively highly correlated with moderate and heavy rainfall distributions but poorly correlated with light rainfall. The results suggested that achieving the precise monitoring and mitigation of drought disasters in Inner Mongolia in the future will require a combined analysis of indicators, including agricultural drought, hydrological drought, and socio-economic drought. Such an approach will enable a comprehensive analysis of drought characteristics under different underlying surface conditions in Inner Mongolia. Full article

In a cell-free massive multiple-input multiple-output (MIMO) system without cells, it is assumed that there are smart jammers and disrupters (SJDs) that attempt to interfere with and eavesdrop on the downlink communications of legitimate users. A secure transmission scheme based on multiple intelligent reflecting surfaces (IRSs) and artificial noise (AN) is proposed. First, an access point (AP) selection strategy based on user location information is designed, which aims to determine the set of APs serving users. Then, a joint optimization framework based on the block coordinate descent (BCD) algorithm is constructed, and a non-convex optimization solution based on the univariate function optimization and semi-definite relaxation (SDR) is proposed with the aim of maximising the minimum achievable secrecy rate for users. By solving the univariate function maximisation problem, the multi-variable coupled non-convex problem is transformed into a solvable convex problem, obtaining the optimal AP beamforming, AN matrix and IRS phase shift matrix. Specifically, in a single-user scenario, the scheme of multiple intelligent reflecting surfaces combined with artificial noise can improve the user’s achievable secrecy rate by about 11% compared to the existing method (single intelligent reflective surface combined with artificial noise) and about 2% compared to the scheme assisted by multiple intelligent reflecting surfaces without artificial noise assistance. Full article

Timely and accurate detection of pavement cracks, the most common type of road damage, is essential for ensuring road safety. Automatic image segmentation of cracks can accurately locate their pixel positions. This paper proposes a Multi-Scale Feature Enhanced U-shaped Network (MFE-UNet) for pavement crack detection. This network model uses a Residual Detail-Enhanced Block (RDEB) instead of a conventional convolution in the encoder–decoder process. The block combines Efficient Multi-Scale Attention to enhance its feature extraction performance. The Multi-Scale Gating Feature Fusion (MGFF) is incorporated into the skip connections, enhancing the fusion of multi-scale features to capture finer crack details while maintaining rich semantic information. Furthermore, we created a pavement crack image dataset named China_MCrack, consisting of 1500 images collected from road surfaces using smartphone-mounted motorbikes. The proposed network was trained and tested on the China_MCrack, DeepCrack, and Crack-Forest datasets, with additional generalization experiments on the BochumCrackDataset. The results were compared with those of the U-Net model, ResUNet, and Attention U-Net. The experimental results show that the proposed MFE-UNet model achieves accuracies of 82.95%, 91.71%, and 69.02% on three datasets, namely, China_MCrack, DeepCrack, and Crack-Forest datasets, respectively, and the F1_score is improved by 1–4% compared with other networks. Experimental results demonstrate that the proposed method is effective in detecting cracks at the pixel level. Full article

Next point-of-interest (POI) recommendation provides users with location suggestions that they may be interested in, allowing them to explore their surroundings. Existing sequence-based or graph-based POI recommendation methods have matured in capturing spatiotemporal information; however, POI recommendation methods based on large language models (LLMs) focus more on capturing sequential transition relationships. This raises an unexplored challenge: how to leverage LLMs to better capture geographic contextual information. To address this, we propose interpretable embeddings for next point-of-interest recommendation via large language model question–answering, named QA-POI, which transforms the POI recommendation task into obtaining interpretable embeddings via LLM prompts, followed by lightweight MLP fine-tuning. We introduce question–answer embeddings, which are generated by asking LLMs yes/no questions about the user’s trajectory sequence. By asking spatiotemporal questions about the trajectory sequence, we aim to extract as much spatiotemporal information from the LLM as possible. During training, QA-POI iteratively selects the most valuable subset of potential questions from a set of questions to prompt the LLM for the next POI recommendation. It is then fine-tuned for the next POI recommendation task using a lightweight Multi-Layer Perceptron (MLP). Extensive experiments on two datasets demonstrate the effectiveness of our approach. Full article

As the climate crisis progresses, droughts and the seasonal availability of fresh water are becoming increasingly common in different regions of the world. One solution to tackle this problem is the reuse of treated wastewater in agriculture. This study was carried out in two significant hydrographic regions located in the southeast of Brazil (Mogi Guaçu River Water Management Unit—UGRHI-09 and Piracicaba River Basin—PRB) that have notable differences in terms of land use and land cover. The aim of this study was to carry out a multi-criteria analysis of a set of environmental attributes in order to classify the areas under study according to their levels of soil suitability and runoff potential. The integrated analysis made it possible to geospatialize prospective regions for reuse, under two specified conditions. In the UGRHI-09, condition 1 corresponds to 3373.24 km², while condition 2 comprises 286.07 km², located mainly in the north-western and central-eastern portions of the unit. In the PRB, condition 1 was also more expressive in occupational terms, corresponding to 1447.83 km²; and condition 2 was perceptible in 53.11 km², predominantly in the central region of the basin. The physical characteristics of the areas studied were decisive in delimiting the areas suitable for the reuse of treated wastewater. Full article